A. Field of the Invention
The present invention relates to a method for auto-cropping a scanned image, especially to a method which can dynamically refine the threshold values of R (Red), G (Green), B (Blue) colors for determining borderline pixels in response to the values of a background image, thereby to precisely determine the crop range.
B. Description of the Prior Art
A conventional method for auto-cropping a scanned image is performed after the process of prescan. The auto-crop technology involves in precisely distinguishing the Area of Interest (hereinafter referred to as AOI) from a background image by finding the borderlines of the AOI. Since the color of the background image is usually black, so the R, G, B values of a background pixel is supposed to be very close to one another. Ideally, the R, G, B values of a background pixel shall be all zeros. In contrast, the colors of the AOI is full of variety. And, the R, G, B values of the AOI pixels shall be non-uniform. Accordingly, it is easy to tell a background image pixel from an AOI pixel by checking the standard differences of its R, G, B values.
Conventionally, a detection procedure is performed by checking the R, G, B, values of each pixel row by row and column by column. In general, if the differences of the R, G, B values of a pixel exceeds a predetermined value, the pixel is determined to be an AOI pixel. If not, the pixel will be determined to be a background pixel. The borderlines of the AOI refer to the background pixels that circumscribes the AOI. To provide a criteria for determining the R, G, B values of a background pixel, the conventional technology applies constant R, G, B threshold values for distinguishing a background pixel from an AOI pixel.
However, the constant R, G, B threshold values can not provide sufficient information for distinguishing a background pixel from an AOI pixel under various circumstances. For instance, when the color of the original itself is darker than the background color, then the background image will be mistaken as part of the AOI if the R, G, B threshold values are set too high. On the other hand, if the R, G, B threshold values are set too low, the AOI pixels will be mistaken as background pixels. Eventually, the R, G, B threshold values determine the precision of auto-cropping.
In fact, distinguishing a background pixel from an AOI pixel is not straightforward. For one reason, the background image is a reflection image from the cover of the scanner. Usually the cover of the scanner is made of black material. When a light source emits light onto the black cover of the scanner, the reflection image of the black cover is suppose to be uniformly black. Nevertheless, once the black material is made of unqualified material or flawed in manufacture, the reflection image will generate unexpected results.
Moreover, the variety of scanner models and the types of light sources should also be considered. Since the intensity of the light will be getting stronger after power-on and then getting stable after a period of time, so the background image will not be uniformly black if scanned during the warm-up process. In such case, if the scanner need to run a warm-up process after power-on or the light source is unstable, then the light intensity of a scanned image will be unevenly distributed. In addition, it is also possible that the color of the original itself may be darker than the color of the cover. Consequently, it is difficult to tell a background pixel from an AOI pixel based on constant R, G, B threshold values.